Reentry trajectory planning optimization based on ant colony algorithm

An optimal trajectory is very important to reusable launch vehicle (RLV) which faces the critical heating and aero force when it comes back from outer space through the dense atmosphere. However, the trajectory planning is a sort of typically large scale and multi-constraint optimization problem. Ant colony algorithm is a new class of population algorithm which has the potential to solve the contradiction between the global optimization and excessive constraint information. In this paper, an optimal trajectory planning for RLV from the beginning reentry state to landing site with minimum accumulated heat load under multi-constraint reentry condition is studied. To achieve this goal, the dynamics of RLV is first described and the cost function of the optimal problem is formulated with multi reentry constraints. Then, a series of discrete trajectory points are designed on reentry corridor. In order to get a fast and precise optimal trajectory, the trajectory points between initial and end states were unevenly plotted according to energy state. Then the process of searching the optimal trajectory for RLV with ant colony algorithm is proposed. This algorithm is implemented on a RLV which is similar to the X-33 and its validity is proven by simulation result.